Segmentation and detection of cattle branding images using CNN and SVM classification
| Autor: | Carlos Eduardo da Rosa Silva, Bruno Belloni, Juliano Weber |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry Computing General Engineering convolutional neural network Pattern recognition cattle branding Computación Informótica Sample (graphics) Convolutional neural network support vector machines lcsh:QA75.5-76.95 Support vector machine Cohen's kappa General Earth and Planetary Sciences Segmentation Artificial intelligence lcsh:Electronic computers. Computer science business Information Technology General Environmental Science |
| Zdroj: | GREDOS: Repositorio Institucional de la Universidad de Salamanca Universidad de Salamanca (USAL) Advances in Distributed Computing and Artificial Intelligence Journal, Vol 8, Iss 2, Pp 19-32 (2020) GREDOS. Repositorio Institucional de la Universidad de Salamanca instname |
| ISSN: | 2255-2863 |
| Popis: | This article presents a hybrid method that uses Convolutional Neural Networks (CNN) to segmentation and Support Vector Machines (SVM) to detection the brandings. The experiments were performed using a cattle branding images. Metrics of Overall Accuracy, Recall, Precision, Kappa Coefficient, and Processing Time were used in order to assess the proposed tool. The results obtained here were satisfactory, reaching a Overall Accuracy of 93% in the first experiment with 39 brandings and 1,950 sample images, and 95% of accuracy in the second experiment, with the same 39 brandings, but with 2,730 sample images. The processing time attained in the experiments was 32s and 42s, respectively. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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